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An Empirical Study of Mobile Code Offloading in Unpredictable Environments
Indexado
WoS WOS:001030578200001
Scopus SCOPUS_ID:85164437024
DOI 10.1109/ACCESS.2023.3292887
Año 2023
Tipo artículo de investigación

Citas Totales

Autores Afiliación Chile

Instituciones Chile

% Participación
Internacional

Autores
Afiliación Extranjera

Instituciones
Extranjeras


Abstract



Mobile code offloading is a well-known technique for enhancing the capabilities of mobile platforms by transparently leveraging the resources to the cloud. Although this technique has been studied for years, little empirical evidence exists to demonstrate its alleged benefits in terms of performance in real-life situations. All studies conducted on this topic have so far been relegated to controlled environments in laboratory settings. As such, there is no evidence of how and how well this technique performs in real-life scenarios, where network unreliability is the norm. In this work, we present the first empirical study of an Android mobile application integrated with a code offloading framework being tested in the wild. We distributed an application that contains a set of benchmarks in APK format and deployed it on a wide gamut of Android devices to which we had no physical access. We carefully detail the methodology and infrastructure we used to monitor the benchmarks' performance of 18 devices. Overall, our results show that the accuracy of the decision-making engine is heavily affected by a couple of factors, mainly the network diagnosis and connection type. Therefore, determining whether or not it is more convenient to execute a given task in the cloud is a difficult task. We summarize five lessons we learned by performing our experiment that we believe should be considered for future experiments in this area.

Revista



Revista ISSN
Ieee Access 2169-3536

Métricas Externas



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Disciplinas de Investigación



WOS
Computer Science, Information Systems
Telecommunications
Engineering, Electrical & Electronic
Scopus
Materials Science (All)
Computer Science (All)
Engineering (All)
SciELO
Sin Disciplinas

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Publicaciones WoS (Ediciones: ISSHP, ISTP, AHCI, SSCI, SCI), Scopus, SciELO Chile.

Colaboración Institucional



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Autores - Afiliación



Ord. Autor Género Institución - País
1 Sanabria, Pablo Hombre Pontificia Universidad Católica de Chile - Chile
Centro Nacional de Inteligencia Artificial (CENIA) - Chile
Centro Nacional de Inteligencia Artificial - Chile
2 Neyem, Andres Hombre Pontificia Universidad Católica de Chile - Chile
Centro Nacional de Inteligencia Artificial (CENIA) - Chile
Centro Nacional de Inteligencia Artificial - Chile
3 Alcocer, Juan Pablo Sandoval Hombre Pontificia Universidad Católica de Chile - Chile
4 Fernandez-Blanco, Alison Mujer Pontificia Universidad Católica de Chile - Chile

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Financiamiento



Fuente
National Agency for Research and Development (ANID)/Scholarship Program/DOCTORADO NACIONAL
ANID FONDECYT Iniciacion Folio
National Center for Artificial Intelligence (CENIA), Basal ANID

Muestra la fuente de financiamiento declarada en la publicación.

Agradecimientos



Agradecimiento
This work was supported in part by the National Agency for Research and Development (ANID)/Scholarship Program/DOCTORADO NACIONAL under Grant 2020-21200979; and in part by the National Center for Artificial Intelligence (CENIA), Basal ANID, under Grant FB210017. The work of Sandoval Alcocer Juan Pablo was supported by ANID FONDECYT Iniciacion Folio under Grant 11220885.

Muestra la fuente de financiamiento declarada en la publicación.